SlideShare a Scribd company logo
1 of 1
Download to read offline
Spatial density approach for space debris
launch modelling
Richard Ottaway (24862436) – supervised by Dr Camilla
Colombo & Francesca Letizia (co-supervisor) Poster Board Number: 77
Time: 09:30 - 12:30
Anticipated final stages of research
This project will demonstrate whether the source/sink approach can be included in the
density based method and will function with the pre-existing spatial density models. A
traffic model will be implemented to study the evolution of space debris in LEO and
hopefully be a valuable contribution to this field of study.
References
[1] Letizia, F., et al. Multidimensional extension of the continuity equation method for debris clouds evolution. Adv. Space Res.
(2015), http://dx.doi.org/10.1016/j.asr.2015.11.035
[2] McInnes, C.R. Simple Analytic Model of the Long-Term Evolution of Nanosatellite Constellations, Journal of Guidance,
Control, and Dynamics, Vol. 23, No. 2 (2000), pp. 332-338. http://dx.doi.org/10.2514/2.4527
[3] Colombo C., Letizia F., Lewis H. G., “Spatial density approach for modelling the space debris population”, The 26th AAS/AIAA
Space Flight Mechanics Meeting, Napa, CA, 14-18 Feb. 2016, AAS 16-465.
[4] ] Gor’kavyi, N., “A new approach to dynamical evolution of interplanetary dust,” The Astrophysical
Journal, Vol. 474, No. 1, 1997, pp. 496–502,
[5] LaFleur, J.M., Extension of a Simple Model for Orbital Debris Proliferation and Mitigation, AAS 11-173, Spaceflight Mechanics
2011, Proceedings of the 21st AAS/AIAA Flight Mechanics Meeting, Feb 13-17, 2011
[6] Space-Track.org (2016) [Accessed 08/07/2016] https://www.space-track.org
Introduction
Space debris poses a huge risk to the operation of spacecraft in Earth orbit. With nearly 20,000 objects larger
than 10cm currently in orbit, it is essential to be able to predict the collision risk any new satellite will
experience during its lifetime. Traditional methods of predicting the path of space debris model each particle
individually, which is computationally expensive. By modelling the space debris environment as a cloud of
varying density, the evolution and movement of space debris can be modelled and used to predict collision
risks in different zones of the orbit.
This project focuses on designing a launch traffic model to model past and future launch data, which will be
inputted into a density cloud model [1]. The model will provide a source term ሶ𝑛+
𝑎, 𝑒 as a function of
𝑎 (Semi Major Axis) and 𝑒 (Eccentricity) to simulate the fast/discontinuous deposition of objects and
injection into orbit due to rocket launches [2]. In order to generate the launch traffic model historical launch
data is analysed and from the past history an extrapolation will be performed.
Progress so far
• Successfully matched approximately 10% of objects in the IADC and Space-Track populations for years
2005-2012.
• Investigated and compared initial distribution of each population
• Read and understood previous works in the field to apply and combine techniques
Sources and Sinks Modelling
In previous work it was proven that the continuity equation (1) can be used to study the time evolution of
the density of fragments in Earth orbit [1] and the debris population in Low Earth Orbit [3]. However, the
source and sink term ሶ𝑛+ − ሶ𝑛− has always been treated as equal to zero for these works. This project uses
historic data from debris populations to compute the ሶ𝑛+
term and input it into the model. The ሶ𝑛+
term
was defined by McInnes [2] and Gorkavyi [4] as equation (2). In this equation the ሶ𝑛 𝑚 term is the mean
number density with the deposition of new satellites centred at orbit radius 𝑟𝑑, and 𝛾 a constant to be
determined.
Aims & Objectives
• Analyse current space debris population
• Create a source function for space debris describing launches
• Implement this source term in debris cloud simulation code
• Analyse results of debris propagation
Initial Debris Distribution
The shaded histograms above illustrate the number of objects and their distribution in Low Earth Orbit,
depending on semi major axis, eccentricity and inclination.
Debris Populations and Initial Debris Distribution
With the aims of creating a traffic model, the current debris population was first analysed with focus on the new
objects launched. Three datasets were considered in this project: Two Line Element (TLE) data from Space-
Track.org [6], data from the IADC (Inter-Agency Space Debris Coordination Committee) and a database from the
UCS (Union of Concerned Scientists). Each database provides different information, as illustrated in Table 1.
It was decided that the UCS database was not complete enough to use. Instead the Space-Track and IADC
populations are being matched and combined to create a database of orbital parameters, mass, size and
classification of each object.
Previous Traffic Models
Many launch traffic models exist. The main model used in debris simulation is based on an 8-year
repeating cycle. A work completed by J. Lafleur [5] describes an alternative launch traffic model, based on
a system of two sinusoidal equations (3)(4). This model is cyclic and mathematically quite simplistic, but it
does not take into account object size or mass, so cannot be used on its own.

More Related Content

What's hot

20150930 Yokohama Protostellar discs
20150930 Yokohama Protostellar discs20150930 Yokohama Protostellar discs
20150930 Yokohama Protostellar discsGareth Murphy
 
AHM 2014: BCube Brokering Framework
AHM 2014: BCube Brokering FrameworkAHM 2014: BCube Brokering Framework
AHM 2014: BCube Brokering FrameworkEarthCube
 
SC7 Workshop 3: Big Data Challenges in Building a Global Earth Observation Sy...
SC7 Workshop 3: Big Data Challenges in Building a Global Earth Observation Sy...SC7 Workshop 3: Big Data Challenges in Building a Global Earth Observation Sy...
SC7 Workshop 3: Big Data Challenges in Building a Global Earth Observation Sy...BigData_Europe
 
Presentation for Osho.Y.B(June 2015)
Presentation for Osho.Y.B(June 2015)Presentation for Osho.Y.B(June 2015)
Presentation for Osho.Y.B(June 2015)Tunde Osho
 
FR4.L10.2: A MICROWAVE SCATTERING MODEL OF VEGETATED SURFACES BASED ON BOR/DD...
FR4.L10.2: A MICROWAVE SCATTERING MODEL OF VEGETATED SURFACES BASED ON BOR/DD...FR4.L10.2: A MICROWAVE SCATTERING MODEL OF VEGETATED SURFACES BASED ON BOR/DD...
FR4.L10.2: A MICROWAVE SCATTERING MODEL OF VEGETATED SURFACES BASED ON BOR/DD...grssieee
 
Flood remedial mesures in gis
Flood remedial mesures in gisFlood remedial mesures in gis
Flood remedial mesures in gisAmitSaha123
 
EAA 2017 Re-engineering the process: How best to share, connect, re-use & pro...
EAA 2017 Re-engineering the process: How best to share, connect, re-use & pro...EAA 2017 Re-engineering the process: How best to share, connect, re-use & pro...
EAA 2017 Re-engineering the process: How best to share, connect, re-use & pro...Keith.May
 
Annapolis Boat show 2014
Annapolis Boat show 2014Annapolis Boat show 2014
Annapolis Boat show 2014Briana Sullivan
 
The key controls in a db system & hydrographs
The key controls in a db system & hydrographsThe key controls in a db system & hydrographs
The key controls in a db system & hydrographsgeographypods
 
Estimation of soil organic carbon stocks in the northeast Tibetan Plateau
Estimation of soil organic carbon stocks in the northeast Tibetan PlateauEstimation of soil organic carbon stocks in the northeast Tibetan Plateau
Estimation of soil organic carbon stocks in the northeast Tibetan PlateauExternalEvents
 
CAA 2015 - Paths Through the Labyrinth
CAA 2015 - Paths Through the LabyrinthCAA 2015 - Paths Through the Labyrinth
CAA 2015 - Paths Through the LabyrinthKeith.May
 
Spatiotemporal Representation Data in R
Spatiotemporal Representation Data in RSpatiotemporal Representation Data in R
Spatiotemporal Representation Data in RLorena Santos
 
3D Analyst - Lake, Jatiluhur
3D Analyst - Lake, Jatiluhur3D Analyst - Lake, Jatiluhur
3D Analyst - Lake, JatiluhurHartanto Sanjaya
 
AHM 2014: The Flow Simulation Tools on VHub
AHM 2014: The Flow Simulation Tools on VHubAHM 2014: The Flow Simulation Tools on VHub
AHM 2014: The Flow Simulation Tools on VHubEarthCube
 
TGS GPS- NE Newfoundland Interpretation
TGS GPS-  NE Newfoundland InterpretationTGS GPS-  NE Newfoundland Interpretation
TGS GPS- NE Newfoundland InterpretationTGS
 
3D Analyst - Lake Lorelindu by GRASS
3D Analyst - Lake Lorelindu by GRASS3D Analyst - Lake Lorelindu by GRASS
3D Analyst - Lake Lorelindu by GRASSHartanto Sanjaya
 

What's hot (20)

20150930 Yokohama Protostellar discs
20150930 Yokohama Protostellar discs20150930 Yokohama Protostellar discs
20150930 Yokohama Protostellar discs
 
AHM 2014: BCube Brokering Framework
AHM 2014: BCube Brokering FrameworkAHM 2014: BCube Brokering Framework
AHM 2014: BCube Brokering Framework
 
Cv
CvCv
Cv
 
SC7 Workshop 3: Big Data Challenges in Building a Global Earth Observation Sy...
SC7 Workshop 3: Big Data Challenges in Building a Global Earth Observation Sy...SC7 Workshop 3: Big Data Challenges in Building a Global Earth Observation Sy...
SC7 Workshop 3: Big Data Challenges in Building a Global Earth Observation Sy...
 
Presentation for Osho.Y.B(June 2015)
Presentation for Osho.Y.B(June 2015)Presentation for Osho.Y.B(June 2015)
Presentation for Osho.Y.B(June 2015)
 
vectorrasterQGIS
vectorrasterQGISvectorrasterQGIS
vectorrasterQGIS
 
The GEO initiative on Carbon and Greenhouse Gases: Integration across domains
The GEO initiative on Carbon and Greenhouse Gases: Integration across domainsThe GEO initiative on Carbon and Greenhouse Gases: Integration across domains
The GEO initiative on Carbon and Greenhouse Gases: Integration across domains
 
FR4.L10.2: A MICROWAVE SCATTERING MODEL OF VEGETATED SURFACES BASED ON BOR/DD...
FR4.L10.2: A MICROWAVE SCATTERING MODEL OF VEGETATED SURFACES BASED ON BOR/DD...FR4.L10.2: A MICROWAVE SCATTERING MODEL OF VEGETATED SURFACES BASED ON BOR/DD...
FR4.L10.2: A MICROWAVE SCATTERING MODEL OF VEGETATED SURFACES BASED ON BOR/DD...
 
Probabilistic Roadmaps
Probabilistic RoadmapsProbabilistic Roadmaps
Probabilistic Roadmaps
 
Flood remedial mesures in gis
Flood remedial mesures in gisFlood remedial mesures in gis
Flood remedial mesures in gis
 
EAA 2017 Re-engineering the process: How best to share, connect, re-use & pro...
EAA 2017 Re-engineering the process: How best to share, connect, re-use & pro...EAA 2017 Re-engineering the process: How best to share, connect, re-use & pro...
EAA 2017 Re-engineering the process: How best to share, connect, re-use & pro...
 
Annapolis Boat show 2014
Annapolis Boat show 2014Annapolis Boat show 2014
Annapolis Boat show 2014
 
The key controls in a db system & hydrographs
The key controls in a db system & hydrographsThe key controls in a db system & hydrographs
The key controls in a db system & hydrographs
 
Estimation of soil organic carbon stocks in the northeast Tibetan Plateau
Estimation of soil organic carbon stocks in the northeast Tibetan PlateauEstimation of soil organic carbon stocks in the northeast Tibetan Plateau
Estimation of soil organic carbon stocks in the northeast Tibetan Plateau
 
CAA 2015 - Paths Through the Labyrinth
CAA 2015 - Paths Through the LabyrinthCAA 2015 - Paths Through the Labyrinth
CAA 2015 - Paths Through the Labyrinth
 
Spatiotemporal Representation Data in R
Spatiotemporal Representation Data in RSpatiotemporal Representation Data in R
Spatiotemporal Representation Data in R
 
3D Analyst - Lake, Jatiluhur
3D Analyst - Lake, Jatiluhur3D Analyst - Lake, Jatiluhur
3D Analyst - Lake, Jatiluhur
 
AHM 2014: The Flow Simulation Tools on VHub
AHM 2014: The Flow Simulation Tools on VHubAHM 2014: The Flow Simulation Tools on VHub
AHM 2014: The Flow Simulation Tools on VHub
 
TGS GPS- NE Newfoundland Interpretation
TGS GPS-  NE Newfoundland InterpretationTGS GPS-  NE Newfoundland Interpretation
TGS GPS- NE Newfoundland Interpretation
 
3D Analyst - Lake Lorelindu by GRASS
3D Analyst - Lake Lorelindu by GRASS3D Analyst - Lake Lorelindu by GRASS
3D Analyst - Lake Lorelindu by GRASS
 

Similar to 2016 Poster Launch Models

ExoSGAN and ExoACGAN: Exoplanet Detection using Adversarial Training Algorithms
ExoSGAN and ExoACGAN: Exoplanet Detection using Adversarial Training AlgorithmsExoSGAN and ExoACGAN: Exoplanet Detection using Adversarial Training Algorithms
ExoSGAN and ExoACGAN: Exoplanet Detection using Adversarial Training AlgorithmsIRJET Journal
 
Building_Complex_Seismic_Velocity_Models_for_Deep_Learning_Inversion.pdf
Building_Complex_Seismic_Velocity_Models_for_Deep_Learning_Inversion.pdfBuilding_Complex_Seismic_Velocity_Models_for_Deep_Learning_Inversion.pdf
Building_Complex_Seismic_Velocity_Models_for_Deep_Learning_Inversion.pdfDeepak Kumar
 
APPLICATION OF SPATIOTEMPORAL ASSOCIATION RULES ON SOLAR DATA TO SUPPORT SPAC...
APPLICATION OF SPATIOTEMPORAL ASSOCIATION RULES ON SOLAR DATA TO SUPPORT SPAC...APPLICATION OF SPATIOTEMPORAL ASSOCIATION RULES ON SOLAR DATA TO SUPPORT SPAC...
APPLICATION OF SPATIOTEMPORAL ASSOCIATION RULES ON SOLAR DATA TO SUPPORT SPAC...IJDKP
 
An innovative idea to discover the trend on multi dimensional spatio-temporal...
An innovative idea to discover the trend on multi dimensional spatio-temporal...An innovative idea to discover the trend on multi dimensional spatio-temporal...
An innovative idea to discover the trend on multi dimensional spatio-temporal...eSAT Journals
 
An innovative idea to discover the trend on multi dimensional spatio-temporal...
An innovative idea to discover the trend on multi dimensional spatio-temporal...An innovative idea to discover the trend on multi dimensional spatio-temporal...
An innovative idea to discover the trend on multi dimensional spatio-temporal...eSAT Publishing House
 
Elliptic Fourier Descriptors in the Study of Cyclone Cloud Intensity Patterns
Elliptic Fourier Descriptors in the Study of Cyclone Cloud Intensity PatternsElliptic Fourier Descriptors in the Study of Cyclone Cloud Intensity Patterns
Elliptic Fourier Descriptors in the Study of Cyclone Cloud Intensity PatternsCSCJournals
 
Intelligent Lunar Landing Site Recommender
Intelligent Lunar Landing Site RecommenderIntelligent Lunar Landing Site Recommender
Intelligent Lunar Landing Site RecommenderDr. Amarjeet Singh
 
Land Cover and Land use Classifiction from Satellite Image Time Series Data u...
Land Cover and Land use Classifiction from Satellite Image Time Series Data u...Land Cover and Land use Classifiction from Satellite Image Time Series Data u...
Land Cover and Land use Classifiction from Satellite Image Time Series Data u...Lorena Santos
 
32.pdf
32.pdf32.pdf
32.pdfa a
 
The 2013 NRC Decadal Survey in Solar and Space Physics (Heliophysics)
The 2013 NRC Decadal Survey in Solar and Space Physics (Heliophysics)The 2013 NRC Decadal Survey in Solar and Space Physics (Heliophysics)
The 2013 NRC Decadal Survey in Solar and Space Physics (Heliophysics)Art Charo
 
Computational Training and Data Literacy for Domain Scientists
Computational Training and Data Literacy for Domain ScientistsComputational Training and Data Literacy for Domain Scientists
Computational Training and Data Literacy for Domain ScientistsJoshua Bloom
 
How can the use of computer simulation benefit the monitoring and mitigation ...
How can the use of computer simulation benefit the monitoring and mitigation ...How can the use of computer simulation benefit the monitoring and mitigation ...
How can the use of computer simulation benefit the monitoring and mitigation ...BrennanMinns
 
20131106 acm geocrowd
20131106 acm geocrowd20131106 acm geocrowd
20131106 acm geocrowdDongpo Deng
 
Trajectory Segmentation and Sampling of Moving Objects Based On Representativ...
Trajectory Segmentation and Sampling of Moving Objects Based On Representativ...Trajectory Segmentation and Sampling of Moving Objects Based On Representativ...
Trajectory Segmentation and Sampling of Moving Objects Based On Representativ...ijsrd.com
 
Locating Hidden Exoplanets in ALMA Data Using Machine Learning
Locating Hidden Exoplanets in ALMA Data Using Machine LearningLocating Hidden Exoplanets in ALMA Data Using Machine Learning
Locating Hidden Exoplanets in ALMA Data Using Machine LearningSérgio Sacani
 

Similar to 2016 Poster Launch Models (20)

2013 newton e daniel
2013 newton e daniel2013 newton e daniel
2013 newton e daniel
 
Space Tug Rendezvous
Space Tug RendezvousSpace Tug Rendezvous
Space Tug Rendezvous
 
ExoSGAN and ExoACGAN: Exoplanet Detection using Adversarial Training Algorithms
ExoSGAN and ExoACGAN: Exoplanet Detection using Adversarial Training AlgorithmsExoSGAN and ExoACGAN: Exoplanet Detection using Adversarial Training Algorithms
ExoSGAN and ExoACGAN: Exoplanet Detection using Adversarial Training Algorithms
 
Building_Complex_Seismic_Velocity_Models_for_Deep_Learning_Inversion.pdf
Building_Complex_Seismic_Velocity_Models_for_Deep_Learning_Inversion.pdfBuilding_Complex_Seismic_Velocity_Models_for_Deep_Learning_Inversion.pdf
Building_Complex_Seismic_Velocity_Models_for_Deep_Learning_Inversion.pdf
 
APPLICATION OF SPATIOTEMPORAL ASSOCIATION RULES ON SOLAR DATA TO SUPPORT SPAC...
APPLICATION OF SPATIOTEMPORAL ASSOCIATION RULES ON SOLAR DATA TO SUPPORT SPAC...APPLICATION OF SPATIOTEMPORAL ASSOCIATION RULES ON SOLAR DATA TO SUPPORT SPAC...
APPLICATION OF SPATIOTEMPORAL ASSOCIATION RULES ON SOLAR DATA TO SUPPORT SPAC...
 
An innovative idea to discover the trend on multi dimensional spatio-temporal...
An innovative idea to discover the trend on multi dimensional spatio-temporal...An innovative idea to discover the trend on multi dimensional spatio-temporal...
An innovative idea to discover the trend on multi dimensional spatio-temporal...
 
An innovative idea to discover the trend on multi dimensional spatio-temporal...
An innovative idea to discover the trend on multi dimensional spatio-temporal...An innovative idea to discover the trend on multi dimensional spatio-temporal...
An innovative idea to discover the trend on multi dimensional spatio-temporal...
 
437FINALREPORTmnras
437FINALREPORTmnras437FINALREPORTmnras
437FINALREPORTmnras
 
21275
2127521275
21275
 
Elliptic Fourier Descriptors in the Study of Cyclone Cloud Intensity Patterns
Elliptic Fourier Descriptors in the Study of Cyclone Cloud Intensity PatternsElliptic Fourier Descriptors in the Study of Cyclone Cloud Intensity Patterns
Elliptic Fourier Descriptors in the Study of Cyclone Cloud Intensity Patterns
 
Intelligent Lunar Landing Site Recommender
Intelligent Lunar Landing Site RecommenderIntelligent Lunar Landing Site Recommender
Intelligent Lunar Landing Site Recommender
 
Land Cover and Land use Classifiction from Satellite Image Time Series Data u...
Land Cover and Land use Classifiction from Satellite Image Time Series Data u...Land Cover and Land use Classifiction from Satellite Image Time Series Data u...
Land Cover and Land use Classifiction from Satellite Image Time Series Data u...
 
32.pdf
32.pdf32.pdf
32.pdf
 
The 2013 NRC Decadal Survey in Solar and Space Physics (Heliophysics)
The 2013 NRC Decadal Survey in Solar and Space Physics (Heliophysics)The 2013 NRC Decadal Survey in Solar and Space Physics (Heliophysics)
The 2013 NRC Decadal Survey in Solar and Space Physics (Heliophysics)
 
Computational Training and Data Literacy for Domain Scientists
Computational Training and Data Literacy for Domain ScientistsComputational Training and Data Literacy for Domain Scientists
Computational Training and Data Literacy for Domain Scientists
 
How can the use of computer simulation benefit the monitoring and mitigation ...
How can the use of computer simulation benefit the monitoring and mitigation ...How can the use of computer simulation benefit the monitoring and mitigation ...
How can the use of computer simulation benefit the monitoring and mitigation ...
 
20131106 acm geocrowd
20131106 acm geocrowd20131106 acm geocrowd
20131106 acm geocrowd
 
The Cosmic V-Web
The Cosmic V-WebThe Cosmic V-Web
The Cosmic V-Web
 
Trajectory Segmentation and Sampling of Moving Objects Based On Representativ...
Trajectory Segmentation and Sampling of Moving Objects Based On Representativ...Trajectory Segmentation and Sampling of Moving Objects Based On Representativ...
Trajectory Segmentation and Sampling of Moving Objects Based On Representativ...
 
Locating Hidden Exoplanets in ALMA Data Using Machine Learning
Locating Hidden Exoplanets in ALMA Data Using Machine LearningLocating Hidden Exoplanets in ALMA Data Using Machine Learning
Locating Hidden Exoplanets in ALMA Data Using Machine Learning
 

2016 Poster Launch Models

  • 1. Spatial density approach for space debris launch modelling Richard Ottaway (24862436) – supervised by Dr Camilla Colombo & Francesca Letizia (co-supervisor) Poster Board Number: 77 Time: 09:30 - 12:30 Anticipated final stages of research This project will demonstrate whether the source/sink approach can be included in the density based method and will function with the pre-existing spatial density models. A traffic model will be implemented to study the evolution of space debris in LEO and hopefully be a valuable contribution to this field of study. References [1] Letizia, F., et al. Multidimensional extension of the continuity equation method for debris clouds evolution. Adv. Space Res. (2015), http://dx.doi.org/10.1016/j.asr.2015.11.035 [2] McInnes, C.R. Simple Analytic Model of the Long-Term Evolution of Nanosatellite Constellations, Journal of Guidance, Control, and Dynamics, Vol. 23, No. 2 (2000), pp. 332-338. http://dx.doi.org/10.2514/2.4527 [3] Colombo C., Letizia F., Lewis H. G., “Spatial density approach for modelling the space debris population”, The 26th AAS/AIAA Space Flight Mechanics Meeting, Napa, CA, 14-18 Feb. 2016, AAS 16-465. [4] ] Gor’kavyi, N., “A new approach to dynamical evolution of interplanetary dust,” The Astrophysical Journal, Vol. 474, No. 1, 1997, pp. 496–502, [5] LaFleur, J.M., Extension of a Simple Model for Orbital Debris Proliferation and Mitigation, AAS 11-173, Spaceflight Mechanics 2011, Proceedings of the 21st AAS/AIAA Flight Mechanics Meeting, Feb 13-17, 2011 [6] Space-Track.org (2016) [Accessed 08/07/2016] https://www.space-track.org Introduction Space debris poses a huge risk to the operation of spacecraft in Earth orbit. With nearly 20,000 objects larger than 10cm currently in orbit, it is essential to be able to predict the collision risk any new satellite will experience during its lifetime. Traditional methods of predicting the path of space debris model each particle individually, which is computationally expensive. By modelling the space debris environment as a cloud of varying density, the evolution and movement of space debris can be modelled and used to predict collision risks in different zones of the orbit. This project focuses on designing a launch traffic model to model past and future launch data, which will be inputted into a density cloud model [1]. The model will provide a source term ሶ𝑛+ 𝑎, 𝑒 as a function of 𝑎 (Semi Major Axis) and 𝑒 (Eccentricity) to simulate the fast/discontinuous deposition of objects and injection into orbit due to rocket launches [2]. In order to generate the launch traffic model historical launch data is analysed and from the past history an extrapolation will be performed. Progress so far • Successfully matched approximately 10% of objects in the IADC and Space-Track populations for years 2005-2012. • Investigated and compared initial distribution of each population • Read and understood previous works in the field to apply and combine techniques Sources and Sinks Modelling In previous work it was proven that the continuity equation (1) can be used to study the time evolution of the density of fragments in Earth orbit [1] and the debris population in Low Earth Orbit [3]. However, the source and sink term ሶ𝑛+ − ሶ𝑛− has always been treated as equal to zero for these works. This project uses historic data from debris populations to compute the ሶ𝑛+ term and input it into the model. The ሶ𝑛+ term was defined by McInnes [2] and Gorkavyi [4] as equation (2). In this equation the ሶ𝑛 𝑚 term is the mean number density with the deposition of new satellites centred at orbit radius 𝑟𝑑, and 𝛾 a constant to be determined. Aims & Objectives • Analyse current space debris population • Create a source function for space debris describing launches • Implement this source term in debris cloud simulation code • Analyse results of debris propagation Initial Debris Distribution The shaded histograms above illustrate the number of objects and their distribution in Low Earth Orbit, depending on semi major axis, eccentricity and inclination. Debris Populations and Initial Debris Distribution With the aims of creating a traffic model, the current debris population was first analysed with focus on the new objects launched. Three datasets were considered in this project: Two Line Element (TLE) data from Space- Track.org [6], data from the IADC (Inter-Agency Space Debris Coordination Committee) and a database from the UCS (Union of Concerned Scientists). Each database provides different information, as illustrated in Table 1. It was decided that the UCS database was not complete enough to use. Instead the Space-Track and IADC populations are being matched and combined to create a database of orbital parameters, mass, size and classification of each object. Previous Traffic Models Many launch traffic models exist. The main model used in debris simulation is based on an 8-year repeating cycle. A work completed by J. Lafleur [5] describes an alternative launch traffic model, based on a system of two sinusoidal equations (3)(4). This model is cyclic and mathematically quite simplistic, but it does not take into account object size or mass, so cannot be used on its own.